Simulations generally encompass a set of sequential sub-processes. One example where simulations are employed is in supply chain logistics, where the goal is to move assets (e.g., equipment, materials and/or food) from a supplier to a customer, passing through one or more places, and potentially involving people and machines. The term logistics refers to the management of resources to accomplish such a goal. Useful information in supply chain logistics typically includes: suppliers, features of products and services; people and machines involved; and time to finish each activity. Such data can be obtained and manipulated directly by means of statistical analysis, as commonly done in the business intelligence area, or indirectly via simulations.
Simulations are typically used to help make decisions. In the supply chain logistics example, simulations provide the ability to observe one or more sub-processes that yield results without actually performing the related activities in the real world. Typically, the level of detail of the entire simulation process is chosen based on the target features of the simulation, e.g., specific simulation behaviors that can be quantified and are important for subsequent analysis and decision making.
Simulation applications may be very complex, and in order to capture the workings of the system, it might be necessary to run each simulation a very large number of times. Thus, extreme computational costs are implied and Big Data strategies are often required.
A need therefore exists for techniques for combining results of previous simulations of portions of a simulated process.